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Abstractive Multi-Document Text Summarization Using a Genetic Algorithm

  • Verónica Neri Mendoza
  • Yulia LedenevaEmail author
  • René Arnulfo García-Hernández
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11524)

Abstract

Multi-Document Text Summarization (MDTS) consists of generating an abstract from a group of two or more number of documents that represent only the most important information of all documents. Generally, the objective is to obtain the main idea of several documents on the same topic. In this paper, we propose a new MDTS method based on a Genetic Algorithm (GA). The fitness function is calculated considering two text features: sentence position and coverage. We propose the binary coding representation, selection, crossover and mutation operators to improve the state-of-the-art results. We test the proposed method on DUC02 data set, specifically, on Abstractive Multi-Document Text Summarization (AMDST) task demonstrating the improvement over the state-of-art methods. Four different tasks for each of the 59 collection of documents (in total 567 documents) are tested. In addition, we test different configurations of the most used methodology to generate AMDST summaries. Moreover, different heuristics such as topline, baseline, baseline-random and lead baseline are calculated. The proposed method for AMDTS demonstrates the improvement over the state-of-art methods and heuristics.

Keywords

Multi-Document Text Summarization (MDTS) Language-independent methods MDTS methodology Genetic algorithm Heuristics 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Autonomous University of the State of MexicoTolucaMexico

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